Psychometric evaluation of the Relative Mastery Scale: An Occupational Adaptation instrument

This study evaluated the psychometric properties of the Relative Mastery Scale (RMS). Valid and reliable client-centered instruments support practice in value-based health care and community-based settings. Participants were 368 community-dwelling adults aged 18 to 95 years. Researchers conducted validity and reliability examinations of the RMS using classical test theory and Rasch measurement model. A partial credit model allowed exploration of individual scale properties. Spearman’s correlation coefficients between items were statistically significant at the .01 level. Cronbach’s alpha coefficient was .94 showing strong internal consistency. In exploratory factor analysis, Factor 1 accounted for 71% of variance with an eigenvalue of 4.26. In Rasch analysis, the 5-point rating scale demonstrated adequate functioning, confirmed unidimensionality, and person/item separation. The RMS instrument demonstrates sound psychometric characteristics. A valid and reliable measure of internal occupational adaptation supports application to monitor progress of internal occupational adaptation across a variety of individuals.

Non-local dispersion and the reassessment of Richardson's t3-scaling law

The Richardson-scaling law states that the mean square separation of a fluid particle pair grows according to t3 within the inertial range and at intermediate times. The theories predicting this scaling regime assume that the pair separation is within the inertial range and that the dispersion is local, which means that only eddies at the scale of the separation contribute. These assumptions ignore the structural organization of the turbulent flow into large-scale shear layers, where the intense small-scale motions are bounded by the large-scale energetic motions. Therefore, the large scales contribute to the velocity difference across the small-scale structures. It is shown that, indeed, the pair dispersion inside these layers is highly non-local and approaches Taylor dispersion in a way that is fundamentally different from the Richardson-scaling law. Also, the layer's contribution to the overall mean square separation remains significant as the Reynolds number increases. This calls into question the validity of the theoretical assumptions. Moreover, a literature survey reveals that, so far, t3 scaling is not observed for initial separations within the inertial range. We propose that the intermediate pair dispersion regime is a transition region that connects the initial Batchelor- with the final Taylor-dispersion regime. Such a simple interpretation is shown to be consistent with observations and is able to explain why t3 scaling is found only for one specific initial separation outside the inertial range. Moreover, the model incorporates the observed non-local contribution to the dispersion, because it requires only small-time-scale properties and large-scale properties.

Psychometric Properties of the Multidimensional Temperance Scale in Adolescents

Recent research has shown the relevance of measuring the virtue of temperance. The present study tested a multidimensional and second-order structure scale to assess temperance using a sub-scale of the Values in Action Inventory of Strengths for Youth (VIA-Youth). Scale properties were tested using data from a sample of 860 adolescents aged from 12 to 18 years old (M = 14.28 years, SD = 1.65). The sample was randomly split into two subsamples for model cross-validation. Using the first sample, we assessed scale dimensionality, measurement invariance, and discriminant and concurrent validity. A second sample was used for model cross-validation. Confirmatory factorial analysis confirmed the fit of one second-order factor temperance virtue model, with the dimensions of forgiveness, modesty, prudence, and self-control. The results indicate scale measurement equivalence across gender and stage of adolescence (early vs. middle). Latent means difference tests showed significant differences in forgiveness, modesty, and self-regulation by gender, and modesty according to adolescence stage. Moreover, the scale showed discriminant and concurrent validity. These findings indicate that this scale is helpful for assessing temperance in adolescents and suggest the value of temperance as a multidimensional and second-order construct.

Sensory-motor cortices shape functional connectivity dynamics in the human brain

Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. We developed a spatially heterogeneous large-scale dynamical circuit model that allowed for variation in local synaptic properties across the human cortex. Here we show that parameterizing local circuit properties with both anatomical and functional gradients generates more realistic static and dynamic resting-state functional connectivity (FC). Furthermore, empirical and simulated FC dynamics demonstrates remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. Time-varying regional fMRI amplitude may track multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggests that sensory-motor regions are a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matches the principal gradient of gene expression that encompasses certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

Complex fitness landscape shapes variation in a hyperpolymorphic species

It is natural to assume that patterns of genetic variation in hyperpolymorphic species can reveal large-scale properties of the fitness landscape that are hard to detect by studying species with ordinary levels of genetic variation1,2. Here, we study such patterns in a fungus Schizophyllum commune, the most polymorphic species known3. Throughout the genome, short-range linkage disequilibrium caused by attraction of rare alleles is higher between pairs of nonsynonymous than of synonymous sites. This effect is especially pronounced for pairs of sites that are located within the same gene, especially if a large fraction of the gene is covered by haploblocks, genome segments where the gene pool consists of two highly divergent haplotypes, which is a signature of balancing selection. Haploblocks are usually shorter than 1000 nucleotides, and collectively cover about 10% of the S. commune genome. LD tends to be substantially higher for pairs of nonsynonymous sites encoding amino acids that interact within the protein. There is a substantial correlation between LDs at the same pairs of nonsynonymous sites in the USA and the Russian populations. These patterns indicate that selection in S. commune involves positive epistasis due to compensatory interactions between nonsynonymous alleles. When less polymorphic species are studied, analogous patterns can be detected only through interspecific comparisons.

Exploring, browsing and interacting with multi-level and multi-scale dynamics of knowledge

The ICT revolution has given birth to a world of digital traces. A wide number of knowledge-driven domains like science are daily fueled by unlimited flows of textual contents. In order to navigate across these growing constellations of words, interdisciplinary innovations are emerging at the crossroad between social and computational sciences. In particular, complex systems approaches make it now possible to reconstruct multi-level and multi-scale dynamics of knowledge by means of inheritance networks of elements of knowledge called phylomemies. In this article, we will introduce an endogenous way to visualize the multi-level and multi-scale properties of phylomemies. The resulting system will enrich a state-of-the-art tree like representation with the possibility to browse through the evolution of a corpus of documents at different level of observation, to interact with various scales of description, to reconstruct a hierarchical clustering of elements of knowledge and to navigate across complex semantic lineages. We will then formalize a generic macro-to-micro methodology of exploration and implement our system as a free software called the Memiescape. Our system will be illustrated by three use cases that will respectively reconstruct the scientific landscape of the top cited publications of the French CNRS, the evolution of the state of the art of knowledge dynamics visualization and the ongoing discovery process of Covid-19 vaccines.

Integrin α5β1 nano-presentation regulates collective keratinocyte migration independent of substrate rigidity

Nanometer-scale properties of the extracellular matrix influence many biological processes, including cell motility. While much information is available for single-cell migration, to date, no knowledge exists on how the nanoscale presentation of extracellular matrix receptors influences collective cell migration. In wound healing, basal keratinocytes collectively migrate on a fibronectin-rich provisional basement membrane to re-epithelialize the injured skin. Among other receptors, the fibronectin receptor integrin α5β1 plays a pivotal role in this process. Using a highly specific integrin α5β1 peptidomimetic combined with nanopatterned hydrogels, we show that keratinocyte sheets regulate their migration ability at an optimal integrin α5β1 nanospacing. This efficiency relies on the effective propagation of stresses within the cell monolayer independent of substrate stiffness. For the first time, this work highlights the importance of extracellular matrix receptor nanoscale organization required for efficient tissue regeneration.

Reconstruction and Synthesis of Source Rock Images at the Pore Scale

Image-based characterization of rock fabric is critical for understanding recovery mechanisms in shale formations due to the significant multiscale nature of shale source rocks. Nanoscale imaging is particularly important for characterizing pore-scale structure of shales. Nanoimaging techniques, however, have a tradeoff between high-resolution/high-contrast sample-destructive imaging modalities and low-contrast/low-resolution sample-preserving modalities. Furthermore, acquisition of nanoscale images is often time-consuming, expensive, and requires signficant levels of expertise, resulting in small image datasets that do not allow for accurate quantification of petrophysical or morphological properties. In this work, we introduce methods for overcoming these challenges in image-based characterization of the fabric of shale source rocks using deep learning models. We present a multimodal/multiscale imaging and characterization workflow for enhancing non-destructive microscopy images of shale. We develop training methods for predicting 3D image volumes from 2D training data and simulate flow through the predicted shale volumes. We then present a novel method for synthesizing porous media images using generative flow models. We apply this method to several datasets, including grayscale and multimodal 3D image volume generation from 2D training images. Results from this work show that the proposed image reconstruction and generation approaches produce realistic pore-scale 3D volumes of shale source rocks even when only 2D image data is available. The models proposed here enable new capabilities for non-destructive imaging of source rocks and we hope will improve our ability to characterize pore-scale properties and phenomena in shales using image data.

Analytical Modeling of Thermodynamic and Transport Anomalies of Water

The structures and properties of biomolecules like proteins, nucleic acids, and membranes depend on water. Water is also very important in industry. Overall, water is an unusual substance with more than 70 anomalous properties. The understanding of water is advancing significantly due to the theoretical and computational modeling. There are different kinds of models, models with fine-scale properties and increasing structural detail with increasing computational expense, and simple models, which focus on global properties of water like thermodynamics, phase diagram and are less computationally expensive. Simplified models give a better understanding of water in ways that complement more complex models. Here, we review analytical modelling of properties of water on different levels, the two- and three-dimensional Mercedes– Benz (MB) models of water and experimental water.